Face Generation 2nd attempt

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
#data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f0eedb29d30>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f0eeda5edd8>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # COMPLETED: Implement Function
    inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='inputs_real')
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name="input_z")
    learning_rate = tf.placeholder(tf.float32, name="learning_rate")
    
    return inputs_real, inputs_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # COMPLETED: Implement Function
    
    # Add alpha (normally present in the arguments)
    alpha = 0.1
    
    with tf.variable_scope('discriminator', reuse=reuse):
        # First hidden layer
        h1 = tf.layers.conv2d(images,
                             64,
                             5,
                             strides=2,
                             padding="same")
        relu1 = tf.maximum(alpha * h1, h1)
        
        # Second hidden layer
        h2 = tf.layers.conv2d(relu1,
                             128,
                             5,
                             strides=1,
                             padding="same")
        batch_norm2 = tf.layers.batch_normalization(h2,
                                                   training=True)
        relu2 = tf.maximum(alpha * batch_norm2, batch_norm2)
        
        # Third hidden layer
        h3 = tf.layers.conv2d(relu2,
                             256,
                             5,
                             strides=2,
                             padding="same")
        batch_norm3 = tf.layers.batch_normalization(h3,
                                                   training=True)
        relu3 = tf.maximum(alpha * batch_norm3, batch_norm3)
        
        # Flatten it
        flat = tf.reshape(relu3, (-1, 7*7*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)

    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    
    alpha = 0.2
    
    # This generator follow the main scheme of the DCGAN exercise
    # COMPLETED: Implement Function
    with tf.variable_scope("generator", reuse=not is_train):
        # First fully connected layer
        h1 = tf.layers.dense(z,
                            7*7*512)
        
        # Reshape
        h1 = tf.reshape(h1, (-1, 7, 7, 512))
        batch_norm1 = tf.layers.batch_normalization(h1, 
                                                    training=is_train)
        relu1 = tf.maximum(alpha * batch_norm1, 
                           batch_norm1)
        
        # Hidden layer 2
        h2 = tf.layers.conv2d_transpose(relu1, 
                                        256, 
                                        5, 
                                        strides=1, 
                                        padding="same")
        batch_norm2 = tf.layers.batch_normalization(h2, 
                                                    training=is_train)
        relu2 = tf.maximum(alpha * batch_norm2, 
                           batch_norm2)
        
        # Hidden layer 3
        h3 = tf.layers.conv2d_transpose(relu2, 
                                        128, 
                                        5, 
                                        strides=2, 
                                        padding="same")
        batch_norm3 = tf.layers.batch_normalization(h3, 
                                                    training=is_train)
        relu3 = tf.maximum(alpha * batch_norm3, 
                           batch_norm3)
        
         # Hidden layer 4
        h4 = tf.layers.conv2d_transpose(relu3, 
                                        64, 
                                        5, 
                                        strides=2, 
                                        padding="same")
        batch_norm4 = tf.layers.batch_normalization(h4, 
                                                    training=is_train)
        relu4 = tf.maximum(alpha * batch_norm3, 
                           batch_norm3)
        
    
        # Output layer
        logits = tf.layers.conv2d_transpose(relu4, 
                                            out_channel_dim, 
                                            5, 
                                            strides=2, 
                                            padding="same")
        out = tf.tanh(logits)
        
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """

    # COMPLETED: Implement Function
    g_model = generator(input_z, out_channel_dim)
    
    d_model_real, d_logits_real = discriminator(input_real)
    
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,
                                                            labels=tf.ones_like(d_model_real)))
    
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
                                                            labels=tf.zeros_like(d_model_fake)))
    
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
                                                            labels=tf.ones_like(d_model_fake)))
    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # COMPLETED: Implement Function
    # Get w and b
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith("discriminator")]
    g_vars = [var for var in t_vars if var.name.startswith("generator")]
    
    # Optimization
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss,
                                                                                 var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss,
                                                                                  var_list=g_vars)
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # COMPLETED: Build Model
    
    tf.reset_default_graph()
    
    # Thanks to the advice by dvsheka for the implementation of the train function   
    input_images, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    
    # Our losses
    d_loss, g_loss = model_loss(input_images, input_z, data_shape[3])
    
    # Our Optimizers
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    i = 0
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                
                # Thanks for the advice thyago
                batch_images = batch_images * 2 
                
                # COMPLETED: Train Model
                i += 1

                # Random noise
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_images: batch_images, input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_images: batch_images, input_z: batch_z, lr: learning_rate})

                if i % 10 == 0:
                
                    train_loss_d = d_loss.eval({input_z: batch_z, input_images: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                    show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)
   
   

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [13]:
batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 = 0.4


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 0.9713... Generator Loss: 1.1662
Epoch 1/2... Discriminator Loss: 2.5831... Generator Loss: 0.7893
Epoch 1/2... Discriminator Loss: 1.2213... Generator Loss: 4.3033
Epoch 1/2... Discriminator Loss: 1.7126... Generator Loss: 0.4862
Epoch 1/2... Discriminator Loss: 1.6306... Generator Loss: 0.3300
Epoch 1/2... Discriminator Loss: 1.3167... Generator Loss: 0.5331
Epoch 1/2... Discriminator Loss: 1.6065... Generator Loss: 0.3146
Epoch 1/2... Discriminator Loss: 1.1754... Generator Loss: 0.5976
Epoch 1/2... Discriminator Loss: 1.1327... Generator Loss: 1.1187
Epoch 1/2... Discriminator Loss: 1.2600... Generator Loss: 1.3040
Epoch 1/2... Discriminator Loss: 0.9529... Generator Loss: 0.8571
Epoch 1/2... Discriminator Loss: 1.3104... Generator Loss: 2.1442
Epoch 1/2... Discriminator Loss: 1.6045... Generator Loss: 0.2880
Epoch 1/2... Discriminator Loss: 0.8957... Generator Loss: 1.2218
Epoch 1/2... Discriminator Loss: 0.8758... Generator Loss: 0.8735
Epoch 1/2... Discriminator Loss: 1.0787... Generator Loss: 2.2934
Epoch 1/2... Discriminator Loss: 1.5862... Generator Loss: 2.8198
Epoch 1/2... Discriminator Loss: 0.8114... Generator Loss: 1.1298
Epoch 1/2... Discriminator Loss: 0.9946... Generator Loss: 1.8194
Epoch 1/2... Discriminator Loss: 0.7681... Generator Loss: 1.2866
Epoch 1/2... Discriminator Loss: 1.0316... Generator Loss: 0.5886
Epoch 1/2... Discriminator Loss: 0.8767... Generator Loss: 0.7550
Epoch 1/2... Discriminator Loss: 1.4396... Generator Loss: 0.3329
Epoch 1/2... Discriminator Loss: 0.8263... Generator Loss: 1.3013
Epoch 1/2... Discriminator Loss: 1.0900... Generator Loss: 0.5585
Epoch 1/2... Discriminator Loss: 1.0623... Generator Loss: 0.6253
Epoch 1/2... Discriminator Loss: 1.0739... Generator Loss: 0.5216
Epoch 1/2... Discriminator Loss: 0.9224... Generator Loss: 1.2607
Epoch 1/2... Discriminator Loss: 1.1998... Generator Loss: 0.5075
Epoch 1/2... Discriminator Loss: 1.1206... Generator Loss: 1.9399
Epoch 1/2... Discriminator Loss: 1.1961... Generator Loss: 0.4749
Epoch 1/2... Discriminator Loss: 1.6409... Generator Loss: 0.2593
---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
/usr/local/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in get_controller(self, default)
   3680       self.stack.append(default)
-> 3681       yield default
   3682     finally:

<ipython-input-13-54fc8bc02454> in <module>()
     14     train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
---> 15           mnist_dataset.shape, mnist_dataset.image_mode)

<ipython-input-11-81e9609e793d> in train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode)
     29         for epoch_i in range(epoch_count):
---> 30             for batch_images in get_batches(batch_size):
     31 

/output/helper.py in get_batches(self, batch_size)
    214                 *self.shape[1:3],
--> 215                 self.image_mode)
    216 

/output/helper.py in get_batch(image_files, width, height, mode)
     87     data_batch = np.array(
---> 88         [get_image(sample_file, width, height, mode) for sample_file in image_files]).astype(np.float32)
     89 

/output/helper.py in <listcomp>(.0)
     87     data_batch = np.array(
---> 88         [get_image(sample_file, width, height, mode) for sample_file in image_files]).astype(np.float32)
     89 

/output/helper.py in get_image(image_path, width, height, mode)
     72     """
---> 73     image = Image.open(image_path)
     74 

/usr/local/lib/python3.5/site-packages/PIL/Image.py in open(fp, mode)
   2311     if filename:
-> 2312         fp = builtins.open(filename, "rb")
   2313 

KeyboardInterrupt: 

During handling of the above exception, another exception occurred:

IndexError                                Traceback (most recent call last)
<ipython-input-13-54fc8bc02454> in <module>()
     13 with tf.Graph().as_default():
     14     train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
---> 15           mnist_dataset.shape, mnist_dataset.image_mode)

/usr/local/lib/python3.5/contextlib.py in __exit__(self, type, value, traceback)
     75                 value = type()
     76             try:
---> 77                 self.gen.throw(type, value, traceback)
     78                 raise RuntimeError("generator didn't stop after throw()")
     79             except StopIteration as exc:

/usr/local/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in get_controller(self, default)
   3682     finally:
   3683       if self._enforce_nesting:
-> 3684         if self.stack[-1] is not default:
   3685           raise AssertionError(
   3686               "Nesting violated for default stack of %s objects"

IndexError: list index out of range

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [ ]:
batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 = 0.4


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 2.2449... Generator Loss: 0.2424
Epoch 1/1... Discriminator Loss: 1.3556... Generator Loss: 0.4426
Epoch 1/1... Discriminator Loss: 0.7136... Generator Loss: 2.3052
Epoch 1/1... Discriminator Loss: 4.1261... Generator Loss: 0.0316
Epoch 1/1... Discriminator Loss: 1.2482... Generator Loss: 1.2085
Epoch 1/1... Discriminator Loss: 2.1881... Generator Loss: 0.1982
Epoch 1/1... Discriminator Loss: 1.0640... Generator Loss: 0.7536
Epoch 1/1... Discriminator Loss: 0.7971... Generator Loss: 1.4204
Epoch 1/1... Discriminator Loss: 1.2528... Generator Loss: 0.4917
Epoch 1/1... Discriminator Loss: 1.2924... Generator Loss: 0.5657
Epoch 1/1... Discriminator Loss: 1.1213... Generator Loss: 0.7248
Epoch 1/1... Discriminator Loss: 1.9685... Generator Loss: 0.2844
Epoch 1/1... Discriminator Loss: 1.2189... Generator Loss: 0.6787
Epoch 1/1... Discriminator Loss: 1.1902... Generator Loss: 1.0207
Epoch 1/1... Discriminator Loss: 1.6994... Generator Loss: 0.2949
Epoch 1/1... Discriminator Loss: 1.6541... Generator Loss: 0.4664
Epoch 1/1... Discriminator Loss: 1.5079... Generator Loss: 0.3942
Epoch 1/1... Discriminator Loss: 1.8974... Generator Loss: 0.2714
Epoch 1/1... Discriminator Loss: 1.7779... Generator Loss: 0.3028
Epoch 1/1... Discriminator Loss: 1.8173... Generator Loss: 0.2702
Epoch 1/1... Discriminator Loss: 1.0554... Generator Loss: 1.2745
Epoch 1/1... Discriminator Loss: 1.4623... Generator Loss: 0.4431
Epoch 1/1... Discriminator Loss: 1.5209... Generator Loss: 0.6417
Epoch 1/1... Discriminator Loss: 1.0792... Generator Loss: 1.7824
Epoch 1/1... Discriminator Loss: 1.3066... Generator Loss: 1.1498
Epoch 1/1... Discriminator Loss: 1.5807... Generator Loss: 0.3580
Epoch 1/1... Discriminator Loss: 1.6400... Generator Loss: 0.3692
Epoch 1/1... Discriminator Loss: 1.5089... Generator Loss: 0.4707
Epoch 1/1... Discriminator Loss: 1.2352... Generator Loss: 0.8424
Epoch 1/1... Discriminator Loss: 1.3377... Generator Loss: 1.3318
Epoch 1/1... Discriminator Loss: 1.7351... Generator Loss: 0.4900
Epoch 1/1... Discriminator Loss: 1.3403... Generator Loss: 1.1327
Epoch 1/1... Discriminator Loss: 1.5697... Generator Loss: 0.6417
Epoch 1/1... Discriminator Loss: 1.8630... Generator Loss: 0.2782
Epoch 1/1... Discriminator Loss: 1.5608... Generator Loss: 0.4345
Epoch 1/1... Discriminator Loss: 1.7701... Generator Loss: 1.3552
Epoch 1/1... Discriminator Loss: 1.3468... Generator Loss: 0.9610
Epoch 1/1... Discriminator Loss: 1.3088... Generator Loss: 0.6602
Epoch 1/1... Discriminator Loss: 1.4469... Generator Loss: 0.8940
Epoch 1/1... Discriminator Loss: 1.4234... Generator Loss: 0.8529
Epoch 1/1... Discriminator Loss: 1.2560... Generator Loss: 1.1698
Epoch 1/1... Discriminator Loss: 1.3854... Generator Loss: 0.9964
Epoch 1/1... Discriminator Loss: 1.2917... Generator Loss: 0.6519
Epoch 1/1... Discriminator Loss: 1.8300... Generator Loss: 0.2682
Epoch 1/1... Discriminator Loss: 1.3951... Generator Loss: 0.6410
Epoch 1/1... Discriminator Loss: 1.3275... Generator Loss: 0.6943
Epoch 1/1... Discriminator Loss: 1.4954... Generator Loss: 0.4305
Epoch 1/1... Discriminator Loss: 1.4296... Generator Loss: 0.4750
Epoch 1/1... Discriminator Loss: 1.4322... Generator Loss: 0.4839
Epoch 1/1... Discriminator Loss: 1.4549... Generator Loss: 0.5049
Epoch 1/1... Discriminator Loss: 1.3456... Generator Loss: 0.6737
Epoch 1/1... Discriminator Loss: 1.3551... Generator Loss: 0.9574
Epoch 1/1... Discriminator Loss: 1.3924... Generator Loss: 0.4598
Epoch 1/1... Discriminator Loss: 1.3373... Generator Loss: 0.7619
Epoch 1/1... Discriminator Loss: 1.4225... Generator Loss: 0.5015
Epoch 1/1... Discriminator Loss: 1.3598... Generator Loss: 0.7372
Epoch 1/1... Discriminator Loss: 1.2553... Generator Loss: 1.0184
Epoch 1/1... Discriminator Loss: 1.2935... Generator Loss: 0.5115
Epoch 1/1... Discriminator Loss: 1.3503... Generator Loss: 0.5397
Epoch 1/1... Discriminator Loss: 1.5273... Generator Loss: 1.5932
Epoch 1/1... Discriminator Loss: 1.3210... Generator Loss: 0.5368
Epoch 1/1... Discriminator Loss: 1.2483... Generator Loss: 0.8561
Epoch 1/1... Discriminator Loss: 1.2679... Generator Loss: 0.8523
Epoch 1/1... Discriminator Loss: 1.5816... Generator Loss: 0.4419
Epoch 1/1... Discriminator Loss: 1.4524... Generator Loss: 0.5548
Epoch 1/1... Discriminator Loss: 1.6276... Generator Loss: 0.3192
Epoch 1/1... Discriminator Loss: 1.7645... Generator Loss: 0.2479
Epoch 1/1... Discriminator Loss: 1.3303... Generator Loss: 0.7273
Epoch 1/1... Discriminator Loss: 1.1657... Generator Loss: 0.7064
Epoch 1/1... Discriminator Loss: 1.5865... Generator Loss: 0.3276
Epoch 1/1... Discriminator Loss: 1.2182... Generator Loss: 0.8285
Epoch 1/1... Discriminator Loss: 1.0089... Generator Loss: 0.9486
Epoch 1/1... Discriminator Loss: 1.3360... Generator Loss: 0.8138
Epoch 1/1... Discriminator Loss: 1.5495... Generator Loss: 1.6495
Epoch 1/1... Discriminator Loss: 1.1952... Generator Loss: 0.9867
Epoch 1/1... Discriminator Loss: 1.3474... Generator Loss: 0.5299
Epoch 1/1... Discriminator Loss: 1.3127... Generator Loss: 0.7019
Epoch 1/1... Discriminator Loss: 1.5413... Generator Loss: 0.3815
Epoch 1/1... Discriminator Loss: 1.2709... Generator Loss: 0.5150
Epoch 1/1... Discriminator Loss: 1.2302... Generator Loss: 1.1267
Epoch 1/1... Discriminator Loss: 1.5476... Generator Loss: 0.6562
Epoch 1/1... Discriminator Loss: 1.3485... Generator Loss: 0.9449
Epoch 1/1... Discriminator Loss: 1.2285... Generator Loss: 0.9356
Epoch 1/1... Discriminator Loss: 1.1900... Generator Loss: 0.7121
Epoch 1/1... Discriminator Loss: 1.7854... Generator Loss: 0.2509
Epoch 1/1... Discriminator Loss: 1.2741... Generator Loss: 1.3149
Epoch 1/1... Discriminator Loss: 1.4570... Generator Loss: 0.4706
Epoch 1/1... Discriminator Loss: 1.5316... Generator Loss: 1.5108
Epoch 1/1... Discriminator Loss: 1.3872... Generator Loss: 0.5611
Epoch 1/1... Discriminator Loss: 0.9320... Generator Loss: 1.2133
Epoch 1/1... Discriminator Loss: 1.5134... Generator Loss: 0.3952
Epoch 1/1... Discriminator Loss: 1.1008... Generator Loss: 1.4631
Epoch 1/1... Discriminator Loss: 1.5510... Generator Loss: 0.8728
Epoch 1/1... Discriminator Loss: 1.1927... Generator Loss: 0.9295
Epoch 1/1... Discriminator Loss: 1.5740... Generator Loss: 0.3350
Epoch 1/1... Discriminator Loss: 1.4047... Generator Loss: 1.0017
Epoch 1/1... Discriminator Loss: 1.1927... Generator Loss: 0.6790
Epoch 1/1... Discriminator Loss: 1.5118... Generator Loss: 0.4166
Epoch 1/1... Discriminator Loss: 1.4464... Generator Loss: 0.4026
Epoch 1/1... Discriminator Loss: 1.5526... Generator Loss: 0.3411
Epoch 1/1... Discriminator Loss: 1.2453... Generator Loss: 0.6178
Epoch 1/1... Discriminator Loss: 1.0344... Generator Loss: 1.1616
Epoch 1/1... Discriminator Loss: 1.8209... Generator Loss: 0.2635
Epoch 1/1... Discriminator Loss: 1.2815... Generator Loss: 0.8030
Epoch 1/1... Discriminator Loss: 1.3815... Generator Loss: 0.4553
Epoch 1/1... Discriminator Loss: 1.2700... Generator Loss: 0.6743
Epoch 1/1... Discriminator Loss: 1.1558... Generator Loss: 1.2126
Epoch 1/1... Discriminator Loss: 1.3678... Generator Loss: 0.4208
Epoch 1/1... Discriminator Loss: 1.3331... Generator Loss: 0.5546
Epoch 1/1... Discriminator Loss: 1.3453... Generator Loss: 0.5197
Epoch 1/1... Discriminator Loss: 1.3274... Generator Loss: 0.8282
Epoch 1/1... Discriminator Loss: 1.0658... Generator Loss: 1.1918
Epoch 1/1... Discriminator Loss: 1.3732... Generator Loss: 1.5487
Epoch 1/1... Discriminator Loss: 1.1751... Generator Loss: 1.7323
Epoch 1/1... Discriminator Loss: 1.3511... Generator Loss: 0.4427
Epoch 1/1... Discriminator Loss: 1.3485... Generator Loss: 0.5246
Epoch 1/1... Discriminator Loss: 1.2491... Generator Loss: 0.6248
Epoch 1/1... Discriminator Loss: 1.7479... Generator Loss: 0.2613
Epoch 1/1... Discriminator Loss: 1.2404... Generator Loss: 0.5905
Epoch 1/1... Discriminator Loss: 1.4893... Generator Loss: 0.5680
Epoch 1/1... Discriminator Loss: 1.3230... Generator Loss: 0.6563
Epoch 1/1... Discriminator Loss: 0.9683... Generator Loss: 1.0065
Epoch 1/1... Discriminator Loss: 1.2423... Generator Loss: 1.1942
Epoch 1/1... Discriminator Loss: 1.0978... Generator Loss: 0.8726
Epoch 1/1... Discriminator Loss: 1.1065... Generator Loss: 0.9495
Epoch 1/1... Discriminator Loss: 1.0129... Generator Loss: 0.8072
Epoch 1/1... Discriminator Loss: 1.1918... Generator Loss: 1.0195
Epoch 1/1... Discriminator Loss: 1.2036... Generator Loss: 0.6943
Epoch 1/1... Discriminator Loss: 1.0909... Generator Loss: 1.1797
Epoch 1/1... Discriminator Loss: 1.3785... Generator Loss: 0.5197
Epoch 1/1... Discriminator Loss: 1.4927... Generator Loss: 0.3939
Epoch 1/1... Discriminator Loss: 1.1497... Generator Loss: 0.6058
Epoch 1/1... Discriminator Loss: 1.3431... Generator Loss: 1.7639
Epoch 1/1... Discriminator Loss: 1.1032... Generator Loss: 1.0242
Epoch 1/1... Discriminator Loss: 1.4298... Generator Loss: 0.4382
Epoch 1/1... Discriminator Loss: 1.6452... Generator Loss: 0.2887
Epoch 1/1... Discriminator Loss: 1.2956... Generator Loss: 0.5243
Epoch 1/1... Discriminator Loss: 1.1149... Generator Loss: 0.7961
Epoch 1/1... Discriminator Loss: 1.1667... Generator Loss: 0.9201
Epoch 1/1... Discriminator Loss: 1.1433... Generator Loss: 0.6731
Epoch 1/1... Discriminator Loss: 1.4719... Generator Loss: 0.3574
Epoch 1/1... Discriminator Loss: 1.1688... Generator Loss: 0.6664
Epoch 1/1... Discriminator Loss: 1.4506... Generator Loss: 2.1237
Epoch 1/1... Discriminator Loss: 1.0233... Generator Loss: 0.8222
Epoch 1/1... Discriminator Loss: 1.0152... Generator Loss: 1.1039
Epoch 1/1... Discriminator Loss: 1.3310... Generator Loss: 0.4351
Epoch 1/1... Discriminator Loss: 1.7597... Generator Loss: 0.3636
Epoch 1/1... Discriminator Loss: 1.5111... Generator Loss: 1.1552
Epoch 1/1... Discriminator Loss: 1.0082... Generator Loss: 0.7848
Epoch 1/1... Discriminator Loss: 1.5357... Generator Loss: 0.3301
Epoch 1/1... Discriminator Loss: 1.3631... Generator Loss: 0.6065
Epoch 1/1... Discriminator Loss: 1.2532... Generator Loss: 0.5325
Epoch 1/1... Discriminator Loss: 1.4143... Generator Loss: 0.4006
Epoch 1/1... Discriminator Loss: 0.9734... Generator Loss: 0.8602
Epoch 1/1... Discriminator Loss: 1.4531... Generator Loss: 0.3651
Epoch 1/1... Discriminator Loss: 1.1309... Generator Loss: 0.9117
Epoch 1/1... Discriminator Loss: 1.1266... Generator Loss: 0.7159
Epoch 1/1... Discriminator Loss: 1.0829... Generator Loss: 1.0841
Epoch 1/1... Discriminator Loss: 1.2626... Generator Loss: 0.8222
Epoch 1/1... Discriminator Loss: 1.2497... Generator Loss: 1.4498
Epoch 1/1... Discriminator Loss: 1.4643... Generator Loss: 0.4204
Epoch 1/1... Discriminator Loss: 1.6208... Generator Loss: 0.2928
Epoch 1/1... Discriminator Loss: 1.4501... Generator Loss: 1.3460
Epoch 1/1... Discriminator Loss: 1.2159... Generator Loss: 0.8263
Epoch 1/1... Discriminator Loss: 1.0579... Generator Loss: 1.1776
Epoch 1/1... Discriminator Loss: 1.6082... Generator Loss: 0.3120
Epoch 1/1... Discriminator Loss: 1.0966... Generator Loss: 0.8625
Epoch 1/1... Discriminator Loss: 0.8719... Generator Loss: 1.2731
Epoch 1/1... Discriminator Loss: 0.9860... Generator Loss: 0.9908
Epoch 1/1... Discriminator Loss: 1.1530... Generator Loss: 0.9572
Epoch 1/1... Discriminator Loss: 1.1869... Generator Loss: 0.6626
Epoch 1/1... Discriminator Loss: 0.8001... Generator Loss: 1.0945
Epoch 1/1... Discriminator Loss: 0.9654... Generator Loss: 1.2195
Epoch 1/1... Discriminator Loss: 1.2525... Generator Loss: 0.4262
Epoch 1/1... Discriminator Loss: 1.0721... Generator Loss: 1.1075
Epoch 1/1... Discriminator Loss: 1.3885... Generator Loss: 0.9221
Epoch 1/1... Discriminator Loss: 1.0193... Generator Loss: 0.9680
Epoch 1/1... Discriminator Loss: 1.2254... Generator Loss: 0.5530
Epoch 1/1... Discriminator Loss: 1.2794... Generator Loss: 0.4635
Epoch 1/1... Discriminator Loss: 1.1089... Generator Loss: 0.9365
Epoch 1/1... Discriminator Loss: 1.4500... Generator Loss: 0.4183
Epoch 1/1... Discriminator Loss: 1.1294... Generator Loss: 0.7090
Epoch 1/1... Discriminator Loss: 0.8551... Generator Loss: 1.4858
Epoch 1/1... Discriminator Loss: 0.9518... Generator Loss: 1.1576
Epoch 1/1... Discriminator Loss: 0.9994... Generator Loss: 1.2540
Epoch 1/1... Discriminator Loss: 1.4353... Generator Loss: 1.2532
Epoch 1/1... Discriminator Loss: 1.2985... Generator Loss: 0.5947
Epoch 1/1... Discriminator Loss: 1.5580... Generator Loss: 1.1516
Epoch 1/1... Discriminator Loss: 1.2430... Generator Loss: 0.5440
Epoch 1/1... Discriminator Loss: 2.2894... Generator Loss: 2.0163
Epoch 1/1... Discriminator Loss: 0.9607... Generator Loss: 1.1485
Epoch 1/1... Discriminator Loss: 1.0354... Generator Loss: 1.1348
Epoch 1/1... Discriminator Loss: 1.2456... Generator Loss: 0.8955
Epoch 1/1... Discriminator Loss: 0.9863... Generator Loss: 0.8536
Epoch 1/1... Discriminator Loss: 1.2790... Generator Loss: 0.5093
Epoch 1/1... Discriminator Loss: 1.1591... Generator Loss: 1.1533
Epoch 1/1... Discriminator Loss: 0.7667... Generator Loss: 1.3803
Epoch 1/1... Discriminator Loss: 1.1441... Generator Loss: 0.9904
Epoch 1/1... Discriminator Loss: 1.2274... Generator Loss: 0.5639
Epoch 1/1... Discriminator Loss: 1.0230... Generator Loss: 1.3707
Epoch 1/1... Discriminator Loss: 1.4390... Generator Loss: 0.4072
Epoch 1/1... Discriminator Loss: 0.8792... Generator Loss: 1.2799
Epoch 1/1... Discriminator Loss: 1.1768... Generator Loss: 1.4679
Epoch 1/1... Discriminator Loss: 1.3520... Generator Loss: 0.6055
Epoch 1/1... Discriminator Loss: 1.1841... Generator Loss: 0.5466
Epoch 1/1... Discriminator Loss: 1.3902... Generator Loss: 0.4518
Epoch 1/1... Discriminator Loss: 1.3137... Generator Loss: 1.1688
Epoch 1/1... Discriminator Loss: 1.5230... Generator Loss: 0.3730
Epoch 1/1... Discriminator Loss: 1.0205... Generator Loss: 1.2833
Epoch 1/1... Discriminator Loss: 1.7645... Generator Loss: 0.2619
Epoch 1/1... Discriminator Loss: 1.0097... Generator Loss: 1.3598
Epoch 1/1... Discriminator Loss: 1.5836... Generator Loss: 0.3500
Epoch 1/1... Discriminator Loss: 1.4223... Generator Loss: 1.8128
Epoch 1/1... Discriminator Loss: 1.1760... Generator Loss: 1.1527
Epoch 1/1... Discriminator Loss: 1.3886... Generator Loss: 0.4360
Epoch 1/1... Discriminator Loss: 0.9832... Generator Loss: 1.0186
Epoch 1/1... Discriminator Loss: 1.3696... Generator Loss: 1.3395
Epoch 1/1... Discriminator Loss: 1.0676... Generator Loss: 0.8246
Epoch 1/1... Discriminator Loss: 1.2858... Generator Loss: 1.0724
Epoch 1/1... Discriminator Loss: 1.0879... Generator Loss: 0.7200
Epoch 1/1... Discriminator Loss: 1.4561... Generator Loss: 0.3709
Epoch 1/1... Discriminator Loss: 1.0366... Generator Loss: 0.7257
Epoch 1/1... Discriminator Loss: 1.3146... Generator Loss: 0.5053
Epoch 1/1... Discriminator Loss: 1.6891... Generator Loss: 0.2832
Epoch 1/1... Discriminator Loss: 1.0896... Generator Loss: 1.0914
Epoch 1/1... Discriminator Loss: 1.7090... Generator Loss: 0.2708
Epoch 1/1... Discriminator Loss: 1.5450... Generator Loss: 0.4902
Epoch 1/1... Discriminator Loss: 0.9426... Generator Loss: 0.7438
Epoch 1/1... Discriminator Loss: 1.2964... Generator Loss: 0.6381
Epoch 1/1... Discriminator Loss: 1.4500... Generator Loss: 0.3496
Epoch 1/1... Discriminator Loss: 1.0078... Generator Loss: 1.3701
Epoch 1/1... Discriminator Loss: 0.9755... Generator Loss: 1.1675
Epoch 1/1... Discriminator Loss: 1.5197... Generator Loss: 0.3276
Epoch 1/1... Discriminator Loss: 0.8278... Generator Loss: 1.8054
Epoch 1/1... Discriminator Loss: 1.4067... Generator Loss: 0.4167
Epoch 1/1... Discriminator Loss: 1.2590... Generator Loss: 0.4408
Epoch 1/1... Discriminator Loss: 0.9150... Generator Loss: 1.0265
Epoch 1/1... Discriminator Loss: 0.9981... Generator Loss: 0.6448
Epoch 1/1... Discriminator Loss: 2.6717... Generator Loss: 2.2423
Epoch 1/1... Discriminator Loss: 1.3907... Generator Loss: 1.5438
Epoch 1/1... Discriminator Loss: 0.8685... Generator Loss: 0.8400
Epoch 1/1... Discriminator Loss: 0.6875... Generator Loss: 1.1415
Epoch 1/1... Discriminator Loss: 1.1047... Generator Loss: 0.5666
Epoch 1/1... Discriminator Loss: 1.0446... Generator Loss: 0.7175
Epoch 1/1... Discriminator Loss: 1.1267... Generator Loss: 1.2181
Epoch 1/1... Discriminator Loss: 0.9566... Generator Loss: 0.8073
Epoch 1/1... Discriminator Loss: 0.7073... Generator Loss: 2.0280
Epoch 1/1... Discriminator Loss: 1.5044... Generator Loss: 0.5340
Epoch 1/1... Discriminator Loss: 1.4034... Generator Loss: 0.3919
Epoch 1/1... Discriminator Loss: 1.3163... Generator Loss: 0.6584
Epoch 1/1... Discriminator Loss: 1.0349... Generator Loss: 0.6665
Epoch 1/1... Discriminator Loss: 1.4108... Generator Loss: 2.0621
Epoch 1/1... Discriminator Loss: 1.0472... Generator Loss: 0.7625
Epoch 1/1... Discriminator Loss: 1.7457... Generator Loss: 0.2698
Epoch 1/1... Discriminator Loss: 1.2037... Generator Loss: 0.5629
Epoch 1/1... Discriminator Loss: 0.8777... Generator Loss: 0.8744
Epoch 1/1... Discriminator Loss: 0.9382... Generator Loss: 1.1729
Epoch 1/1... Discriminator Loss: 1.1780... Generator Loss: 0.6208
Epoch 1/1... Discriminator Loss: 0.6125... Generator Loss: 2.5978
Epoch 1/1... Discriminator Loss: 1.5932... Generator Loss: 1.1433
Epoch 1/1... Discriminator Loss: 0.7289... Generator Loss: 1.3158
---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
/usr/local/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in get_controller(self, default)
   3680       self.stack.append(default)
-> 3681       yield default
   3682     finally:

<ipython-input-15-165f0ae9c0e5> in <module>()
     14     train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
---> 15           celeba_dataset.shape, celeba_dataset.image_mode)

<ipython-input-11-81e9609e793d> in train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode)
     29         for epoch_i in range(epoch_count):
---> 30             for batch_images in get_batches(batch_size):
     31 

/output/helper.py in get_batches(self, batch_size)
    214                 *self.shape[1:3],
--> 215                 self.image_mode)
    216 

/output/helper.py in get_batch(image_files, width, height, mode)
     87     data_batch = np.array(
---> 88         [get_image(sample_file, width, height, mode) for sample_file in image_files]).astype(np.float32)
     89 

/output/helper.py in <listcomp>(.0)
     87     data_batch = np.array(
---> 88         [get_image(sample_file, width, height, mode) for sample_file in image_files]).astype(np.float32)
     89 

/output/helper.py in get_image(image_path, width, height, mode)
     72     """
---> 73     image = Image.open(image_path)
     74 

/usr/local/lib/python3.5/site-packages/PIL/Image.py in open(fp, mode)
   2311     if filename:
-> 2312         fp = builtins.open(filename, "rb")
   2313 

KeyboardInterrupt: 

During handling of the above exception, another exception occurred:

IndexError                                Traceback (most recent call last)
<ipython-input-15-165f0ae9c0e5> in <module>()
     13 with tf.Graph().as_default():
     14     train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
---> 15           celeba_dataset.shape, celeba_dataset.image_mode)

/usr/local/lib/python3.5/contextlib.py in __exit__(self, type, value, traceback)
     75                 value = type()
     76             try:
---> 77                 self.gen.throw(type, value, traceback)
     78                 raise RuntimeError("generator didn't stop after throw()")
     79             except StopIteration as exc:

/usr/local/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in get_controller(self, default)
   3682     finally:
   3683       if self._enforce_nesting:
-> 3684         if self.stack[-1] is not default:
   3685           raise AssertionError(
   3686               "Nesting violated for default stack of %s objects"

IndexError: list index out of range

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.